Generalization-Enhanced Channel Estimation Through Adaptive Interpolation and Multi-Task Learning-Based Denoising Network

Abstract

Accurate CSI estimation with low pilots is desirable for the multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. In the existing channel estimation methods, both interpolation and denoising suffer from the problem of generalization. In this paper, we propose an adaptive interpolation and multi-task learning denoising network for generalization-enhanced CSI estimation. First, we model the wireless channel based on Gaussian process (GP) and use Bayesian optimization (BO) to find the optimal parameters of the Matérn kernel for interpolation. For each matrix, we can find the most suitable parameters of the kernel to achieve precise interpolation adaptively. Then, we design the multi-task residual network (MT-Net) based on multi-task learning. In MT-Net, shared layers are employed to utilize the relevant information between multiple tasks. And task-specific layers are also designed to extract the characteristics of each task. Compared to single-task learning, MT-Net can achieve information sharing between multiple tasks to enhance the scenario generalization of the model. Simulation results show that when the application scenario changes, our method exhibits a stronger generalization ability compared to other neural network-assisted methods.